ImageGen-CoT: Enhancing Text-to-Image In-context Learning with Chain-of-Thought Reasoning
This addresses a bottleneck in multimodal LLMs for generating images from text with contextual reasoning, representing a novel method rather than an incremental improvement.
The paper tackles the problem of contextual reasoning in Text-to-Image In-Context Learning (T2I-ICL) by proposing ImageGen-CoT, a framework that incorporates chain-of-thought reasoning before image generation, resulting in an 80% performance gain for SEED-X on T2I-ICL tasks.
In this work, we study the problem of Text-to-Image In-Context Learning (T2I-ICL). While Unified Multimodal LLMs (MLLMs) have advanced rapidly in recent years, they struggle with contextual reasoning in T2I-ICL scenarios. To address this limitation, we propose a novel framework that incorporates a thought process called ImageGen-CoT prior to image generation. To avoid generating unstructured ineffective reasoning steps, we develop an automatic pipeline to curate a high-quality ImageGen-CoT dataset. We then fine-tune MLLMs using this dataset to enhance their contextual reasoning capabilities. To further enhance performance, we explore test-time scale-up strategies and propose a novel hybrid scaling approach. This approach first generates multiple ImageGen-CoT chains and then produces multiple images for each chain via sampling. Extensive experiments demonstrate the effectiveness of our proposed method. Notably, fine-tuning with the ImageGen-CoT dataset leads to a substantial 80\% performance gain for SEED-X on T2I-ICL tasks. See our project page at https://ImageGen-CoT.github.io/. Code and model weights will be open-sourced.